Weight adjustment of the quantum layer in a hybrid model for skin cancer image classification

Daniel Alejandro Lopez; Oscar Montiel; Miguel Lopez-Montiel; Oscar Castillo

doi:10.1117/12.3027860

30 September 2024 Weight adjustment of the quantum layer in a hybrid model for skin cancer image classification

Daniel Alejandro Lopez, Oscar Montiel, Miguel Lopez-Montiel, Oscar Castillo

Author Affiliations +

Proceedings Volume 13136, Optics and Photonics for Information Processing XVIII; 131360I (2024) https://doi.org/10.1117/12.3027860
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

Integrating quantum algorithms with machine and deep learning models has emerged as a promising method for addressing medical image classification challenges. This integration can enhance speed and efficiency when performing complex computations. However, hybrid quantum models, particularly on Quantum Convolutional Neural Networks (QCNNs) face two significant drawbacks: the placement of the quantum convolutional layer before the model architecture and the lack of integration of the quantum layer within the training process. These disadvantages reduce the robustness and reproducibility of the models. This study proposes that integrates the quantum layer into the quantum layer to address these shortcomings. We present a comparative analysis between a hybrid quantum deep learning model, which includes a trainable quantum layer, and its classical counterpart for the classification of skin cancer dermatoscopic images. The hybrid model attains 0.7865 of accuracy, a recall of 0.7321, a precision of 0.7268, and an F1 Score of 0.7288, while the classical model reaches an accuracy, recall, precision, and F1 Score of 0.8510, 0.8472, 0.8495, and 0.8447. The hybrid model achieves comparable results to its classical counterpart and demonstrates the advantages of weight adjustment in quantum layers and their potential in improving medical imaging analysis.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Daniel Alejandro Lopez, Oscar Montiel, Miguel Lopez-Montiel, and Oscar Castillo "Weight adjustment of the quantum layer in a hybrid model for skin cancer image classification", Proc. SPIE 13136, Optics and Photonics for Information Processing XVIII, 131360I (30 September 2024); https://doi.org/10.1117/12.3027860

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